Substation apparatus failure plays a major role in reliability of power delivery systems. Traditionally, most utilities perform regular maintenance in order to prevent equipment breakdown. Condition-based maintenance strategy monitors the condition of the equipment by measuring and analyzing key parameters and recommends optimum maintenance actions. Equipment such as transformers and standby batteries which are valuable and critical assets in substations has attracted increased attentions in recently years. An automated monitoring and diagnosis tool for power transformers based on dissolved gas analysis, ANNEPS v4.0, was developed. The new tool extended the existing expert system and artificial neural network diagnostic engine with automated data acquisition, display, archiving, and alarm notification functions. This thesis also studied substation batteries types and failure mode and surveyed the market of current on-line battery monitors. A practical battery monitoring system architecture was proposed. Analysis rules of measured parameters were developed. The above study and results can provide basics for further designing of a simple battery monitoring system in industry applications. / Master of Science